ArticleStatistics in medicine2022
A doubly robust method to handle missing multilevel outcome data with application to the China Health and Nutrition Survey.
Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It reports registered trial NCT01794143. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study
Who cites it
4 citing papers in PubMed.
- A two-stage joint model approach to handle incomplete time dependent markers in survival data through inverse probability weight and multiple imputation.Scientific reports · 2025Article
- Leveraging informative missing data to learn about acute respiratory distress syndrome and mortality in long-term hospitalized COVID-19 patients throughout the years of the pandemic.medRxiv : the preprint server for health sciences · 2023Article
- Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?Journal of biomedical informatics · 2023Article
- Leveraging informative missing data to learn about acute respiratory distress syndrome and mortality in long-term hospitalized COVID-19 patients throughout the years of the pandemic.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Missing data are common in longitudinal cohort studies and can lead to bias, particularly in studies with informative missingness. Many common methods for handling informatively missing data in survey samples require correctly specifying a model for missingness. Although doubly robust methods exist to provide unbiased regression coefficients in the presence of missing outcome data, these methods do not account for correlation due to clustering inherent in longitudinal or cluster-sampled studies. In this work, we developed a doubly robust method to estimate the regression of an outcome on a predictor in the presence of missing multilevel data on the outcome, which results in consistent estimation of regression coefficients assuming correct specification of either (1) the probability of missingness or (2) the outcome model. This method involves specification of separate hierarchical models for missingness and for the outcome, conditional on observed auxiliary variables and cluster-specific random effects, to account for correlation among observations. We showed this proposed estimator is doubly robust and derived its asymptotic distribution, conducted simulation studies to compare the method to an existing doubly robust method developed for independent data, and applied the method to data from the China Health and Nutrition Survey, an ongoing multilevel longitudinal cohort study.
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Registered trials
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